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      <h1 class="site-logo" id="site-title">深入浅出PyTorch</h1>
      
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  <a class="reference internal" href="../%E7%AC%AC%E4%B8%80%E7%AB%A0/index.html">
   第一章：PyTorch的简介和安装
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     1.1 PyTorch简介
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     1.2 PyTorch的安装
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     1.3 PyTorch相关资源
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   第二章：PyTorch基础知识
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     2.1 张量
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     2.2 自动求导
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     2.3 并行计算简介
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   第三章：PyTorch的主要组成模块
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     3.1 思考：完成深度学习的必要部分
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     3.2 基本配置
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     3.3 数据读入
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     3.4 模型构建
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     3.5 模型初始化
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     3.6 损失函数
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     3.7 训练和评估
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     3.8 可视化
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     3.9 Pytorch优化器
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   第四章：PyTorch基础实战
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     基础实战——FashionMNIST时装分类
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   第五章：PyTorch模型定义
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     5.1 PyTorch模型定义的方式
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     5.2 利用模型块快速搭建复杂网络
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     5.3 PyTorch修改模型
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     5.4 PyTorch模型保存与读取
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   第六章：PyTorch进阶训练技巧
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     6.1 自定义损失函数
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     6.2 动态调整学习率
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     6.3 模型微调-torchvision
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     6.3 模型微调 - timm
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     6.4 半精度训练
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     6.5 数据增强-imgaug
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/6.6%20%E4%BD%BF%E7%94%A8argparse%E8%BF%9B%E8%A1%8C%E8%B0%83%E5%8F%82.html">
     6.6 使用argparse进行调参
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/PyTorch%E6%A8%A1%E5%9E%8B%E5%AE%9A%E4%B9%89%E4%B8%8E%E8%BF%9B%E9%98%B6%E8%AE%AD%E7%BB%83%E6%8A%80%E5%B7%A7.html">
     PyTorch模型定义与进阶训练技巧
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   第七章：PyTorch可视化
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     7.1 可视化网络结构
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    <a class="reference internal" href="../%E7%AC%AC%E4%B8%83%E7%AB%A0/7.2%20CNN%E5%8D%B7%E7%A7%AF%E5%B1%82%E5%8F%AF%E8%A7%86%E5%8C%96.html">
     7.2 CNN可视化
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     7.3 使用TensorBoard可视化训练过程
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   第八章：PyTorch生态简介
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     8.1 本章简介
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     8.2 torchvision
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     8.3 PyTorchVideo简介
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     8.4 torchtext简介
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     transforms实战
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                <h1>3.5 模型初始化</h1>
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  <section class="tex2jax_ignore mathjax_ignore" id="id1">
<h1>3.5 模型初始化<a class="headerlink" href="#id1" title="永久链接至标题">#</a></h1>
<p>在深度学习模型的训练中，权重的初始值极为重要。一个好的权重值，会使模型收敛速度提高，使模型准确率更精确。为了利于训练和减少收敛时间，我们需要对模型进行合理的初始化。PyTorch也在<code class="docutils literal notranslate"><span class="pre">torch.nn.init</span></code>中为我们提供了常用的初始化方法。
通过本章学习，你将学习到以下内容：</p>
<ul class="simple">
<li><p>常见的初始化函数</p></li>
<li><p>初始化函数的使用</p></li>
</ul>
<section id="torch-nn-init">
<h2>torch.nn.init内容<a class="headerlink" href="#torch-nn-init" title="永久链接至标题">#</a></h2>
<p>通过访问torch.nn.init的官方文档<a class="reference external" href="https://pytorch.org/docs/stable/nn.init.html">链接</a> ，我们发现<code class="docutils literal notranslate"><span class="pre">torch.nn.init</span></code>提供了以下初始化方法：
1 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.uniform_</span></code>(tensor, a=0.0, b=1.0)
2 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.normal_</span></code>(tensor, mean=0.0, std=1.0)
3 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.constant_</span></code>(tensor, val)
4 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.ones_</span></code>(tensor)
5 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.zeros_</span></code>(tensor)
6 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.eye_</span></code>(tensor)
7 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.dirac_</span></code>(tensor, groups=1)
8 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.xavier_uniform_</span></code>(tensor, gain=1.0)
9 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.xavier_normal_</span></code>(tensor, gain=1.0)
10 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.kaiming_uniform_</span></code>(tensor, a=0, mode='fan__in', nonlinearity='leaky_relu')
11 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.kaiming_normal_</span></code>(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')
12 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.orthogonal_</span></code>(tensor, gain=1)
13 . <code class="docutils literal notranslate"><span class="pre">torch.nn.init.sparse_</span></code>(tensor, sparsity, std=0.01)
14 .  <code class="docutils literal notranslate"><span class="pre">torch.nn.init.calculate_gain</span></code>(nonlinearity, param=None)
关于计算增益如下表：</p>
<table class="colwidths-auto table">
<thead>
<tr class="row-odd"><th class="head"><p>nonlinearity</p></th>
<th class="head"><p>gain</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Linear/Identity</p></td>
<td><p>1</p></td>
</tr>
<tr class="row-odd"><td><p>Conv{1,2,3}D</p></td>
<td><p>1</p></td>
</tr>
<tr class="row-even"><td><p>Sigmod</p></td>
<td><p>1</p></td>
</tr>
<tr class="row-odd"><td><p>Tanh</p></td>
<td><p>5/3</p></td>
</tr>
<tr class="row-even"><td><p>ReLU</p></td>
<td><p>sqrt(2)</p></td>
</tr>
<tr class="row-odd"><td><p>Leaky Relu</p></td>
<td><p>sqrt(2/1+neg_slop^2)</p></td>
</tr>
</tbody>
</table>
<p>我们可以发现这些函数除了<code class="docutils literal notranslate"><span class="pre">calculate_gain</span></code>，所有函数的后缀都带有下划线，意味着这些函数将会直接原地更改输入张量的值。</p>
</section>
<section id="id2">
<h2>torch.nn.init使用<a class="headerlink" href="#id2" title="永久链接至标题">#</a></h2>
<p>我们通常会根据实际模型来使用<code class="docutils literal notranslate"><span class="pre">torch.nn.init</span></code>进行初始化，通常使用<code class="docutils literal notranslate"><span class="pre">isinstance</span></code>来进行判断模块（回顾3.4模型构建）属于什么类型。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>

<span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>

<span class="nb">isinstance</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">)</span>
<span class="nb">isinstance</span><span class="p">(</span><span class="n">linear</span><span class="p">,</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kc">True</span>
<span class="kc">False</span>
</pre></div>
</div>
<p>对于不同的类型层，我们就可以设置不同的权值初始化的方法。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 查看随机初始化的conv参数</span>
<span class="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span>
<span class="c1"># 查看linear的参数</span>
<span class="n">linear</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">tensor</span><span class="p">([[[[</span> <span class="mf">0.1174</span><span class="p">,</span>  <span class="mf">0.1071</span><span class="p">,</span>  <span class="mf">0.2977</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.2634</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0583</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2465</span><span class="p">],</span>
          <span class="p">[</span> <span class="mf">0.1726</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0452</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2354</span><span class="p">]]],</span>
        <span class="p">[[[</span> <span class="mf">0.1382</span><span class="p">,</span>  <span class="mf">0.1853</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1515</span><span class="p">],</span>
          <span class="p">[</span> <span class="mf">0.0561</span><span class="p">,</span>  <span class="mf">0.2798</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2488</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.1288</span><span class="p">,</span>  <span class="mf">0.0031</span><span class="p">,</span>  <span class="mf">0.2826</span><span class="p">]]],</span>
        <span class="p">[[[</span> <span class="mf">0.2655</span><span class="p">,</span>  <span class="mf">0.2566</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1276</span><span class="p">],</span>
          <span class="p">[</span> <span class="mf">0.1905</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1308</span><span class="p">,</span>  <span class="mf">0.2933</span><span class="p">],</span>
          <span class="p">[</span> <span class="mf">0.0557</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1880</span><span class="p">,</span>  <span class="mf">0.0669</span><span class="p">]]]])</span>

<span class="n">tensor</span><span class="p">([[</span><span class="o">-</span><span class="mf">0.0089</span><span class="p">,</span>  <span class="mf">0.1186</span><span class="p">,</span>  <span class="mf">0.1213</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2569</span><span class="p">,</span>  <span class="mf">0.1381</span><span class="p">,</span>  <span class="mf">0.3125</span><span class="p">,</span>  <span class="mf">0.1118</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0063</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2330</span><span class="p">,</span>  <span class="mf">0.1956</span><span class="p">]])</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 对conv进行kaiming初始化</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span>
<span class="c1"># 对linear进行常数初始化</span>
<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">linear</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">linear</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">tensor</span><span class="p">([[[[</span> <span class="mf">0.3249</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0500</span><span class="p">,</span>  <span class="mf">0.6703</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.3561</span><span class="p">,</span>  <span class="mf">0.0946</span><span class="p">,</span>  <span class="mf">0.4380</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.9426</span><span class="p">,</span>  <span class="mf">0.9116</span><span class="p">,</span>  <span class="mf">0.4374</span><span class="p">]]],</span>
        <span class="p">[[[</span> <span class="mf">0.6727</span><span class="p">,</span>  <span class="mf">0.9885</span><span class="p">,</span>  <span class="mf">0.1635</span><span class="p">],</span>
          <span class="p">[</span> <span class="mf">0.7218</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.2841</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2970</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.9128</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1134</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.3846</span><span class="p">]]],</span>
        <span class="p">[[[</span> <span class="mf">0.2018</span><span class="p">,</span>  <span class="mf">0.4668</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0937</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.2701</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.3073</span><span class="p">,</span>  <span class="mf">0.6686</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.3269</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0094</span><span class="p">,</span>  <span class="mf">0.3246</span><span class="p">]]]])</span>
<span class="n">tensor</span><span class="p">([[</span><span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">,</span><span class="mf">0.3000</span><span class="p">]])</span>
</pre></div>
</div>
</section>
<section id="id3">
<h2>初始化函数的封装<a class="headerlink" href="#id3" title="永久链接至标题">#</a></h2>
<p>人们常常将各种初始化方法定义为一个<code class="docutils literal notranslate"><span class="pre">initialize_weights()</span></code>的函数并在模型初始后进行使用。</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">initialize_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
	<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
		<span class="c1"># 判断是否属于Conv2d</span>
		<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
			<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
			<span class="c1"># 判断是否有偏置</span>
			<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
				<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">,</span><span class="mf">0.3</span><span class="p">)</span>
		<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">):</span>
			<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
			<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
				<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
		<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
			<span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> 		 
			<span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zeros_</span><span class="p">()</span>	
</pre></div>
</div>
<p>这段代码流程是遍历当前模型的每一层，然后判断各层属于什么类型，然后根据不同类型层，设定不同的权值初始化方法。我们可以通过下面的例程进行一个简短的演示：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 模型的定义</span>
<span class="k">class</span> <span class="nc">MLP</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
  <span class="c1"># 声明带有模型参数的层，这里声明了两个全连接层</span>
  <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="c1"># 调用MLP父类Block的构造函数来进行必要的初始化。这样在构造实例时还可以指定其他函数</span>
    <span class="nb">super</span><span class="p">(</span><span class="n">MLP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
    <span class="bp">self</span><span class="o">.</span><span class="n">hidden</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
    <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
    <span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
    
   <span class="c1"># 定义模型的前向计算，即如何根据输入x计算返回所需要的模型输出</span>
  <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
    <span class="n">o</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">o</span><span class="p">)</span>

<span class="n">mlp</span> <span class="o">=</span> <span class="n">MLP</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">mlp</span><span class="o">.</span><span class="n">parameters</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-------初始化-------&quot;</span><span class="p">)</span>

<span class="n">initialize_weights</span><span class="p">(</span><span class="n">mlp</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">mlp</span><span class="o">.</span><span class="n">parameters</span><span class="p">()))</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
<span class="n">tensor</span><span class="p">([[[[</span> <span class="mf">0.2103</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1679</span><span class="p">,</span>  <span class="mf">0.1757</span><span class="p">],</span>
          <span class="p">[</span><span class="o">-</span><span class="mf">0.0647</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0136</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0410</span><span class="p">],</span>
          <span class="p">[</span> <span class="mf">0.1371</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1738</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0850</span><span class="p">]]]],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
<span class="n">tensor</span><span class="p">([</span><span class="mf">0.2507</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
<span class="n">tensor</span><span class="p">([[</span> <span class="mf">0.2790</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1247</span><span class="p">,</span>  <span class="mf">0.2762</span><span class="p">,</span>  <span class="mf">0.1149</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2121</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.3022</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1859</span><span class="p">,</span>  <span class="mf">0.2983</span><span class="p">,</span>
         <span class="o">-</span><span class="mf">0.0757</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2868</span><span class="p">]],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
<span class="n">tensor</span><span class="p">([</span><span class="o">-</span><span class="mf">0.0905</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
<span class="s2">&quot;-------初始化-------&quot;</span>
<span class="p">[</span><span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
 <span class="n">tensor</span><span class="p">([[[[</span><span class="o">-</span><span class="mf">0.3196</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0204</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5784</span><span class="p">],</span>
           <span class="p">[</span> <span class="mf">0.2660</span><span class="p">,</span>  <span class="mf">0.2242</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.4198</span><span class="p">],</span>
           <span class="p">[</span><span class="o">-</span><span class="mf">0.0952</span><span class="p">,</span>  <span class="mf">0.6033</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.8108</span><span class="p">]]]],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
 <span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
 <span class="n">tensor</span><span class="p">([</span><span class="mf">0.3000</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
 <span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
 <span class="n">tensor</span><span class="p">([[</span> <span class="mf">0.7542</span><span class="p">,</span>  <span class="mf">0.5796</span><span class="p">,</span>  <span class="mf">2.2963</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1814</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.9627</span><span class="p">,</span>  <span class="mf">1.9044</span><span class="p">,</span>  <span class="mf">0.4763</span><span class="p">,</span>  <span class="mf">1.2077</span><span class="p">,</span>
           <span class="mf">0.8583</span><span class="p">,</span>  <span class="mf">1.9494</span><span class="p">]],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
 <span class="n">Parameter</span> <span class="n">containing</span><span class="p">:</span>
 <span class="n">tensor</span><span class="p">([</span><span class="mf">0.</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
</pre></div>
</div>
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